1. Field of the Invention
This invention pertains generally to wireless communication and, more particularly, to systems employing wireless communication, such as, for example, a wireless local area network (WLAN) or a low rate-wireless personal area network (LR-WPAN). The invention also relates to methods for configuring pairs of nodes in a wireless communication network.
2. Background Information
Wireless communication networks are an emerging new technology, which allows users to access information and services electronically, regardless of their geographic position.
In contrast to wired networks, mesh-type, low rate-wireless personal area network (LR-WPAN) wireless communication networks are intended to be relatively low power, to be self-configuring, and to not require any communication infrastructure (e.g., wires) other than power sources and wireless access points.
Relatively low power, radio frequency (RF) lighting control systems employ battery powered, RF switch “sensors”. Such a sensor sends a signal to a remote power control device, such as relay, in order to turn one or more house lights on and off.
One attractive feature in wireless sensor networks is the potential of having relatively lower deployment costs in comparison to an equivalent wired network due to the savings in cabling, labor, materials, testing and verification. In building automation, for example, the installation cost of a light switch in a building facility can be as high as about 10 to 30 times the cost of the light switch. This estimate does not include the possibility of additional work, such as, for example, conduit installation and infrastructure work.
It is believed that there are two principal unresolved issues in wireless sensor intelligent pairing that challenge those advantages: (1) correctly pairing all of the wireless devices at a large scale facility traditionally takes either relatively long configuration times or, else, requires specialized personnel and equipment; and (2) the effort of adding and configuring additional sensors can be as large as the initial network setup effort.
Hence, such sensor intelligent pairing represents a key barrier for the vision of pervasive wireless sensor networks, due to the need to minimize user intervention during deployment and maintenance. This challenge is further increased when a relatively large population of sensing devices needs to be paired to corresponding controlling devices.
Unlike the cable that connects two devices in a wired network (e.g., a light switch and a lamp), a wireless communication network does not provide a physical indication of which device is to be associated on the other end of the line. This particular piece of information that characterizes the pairing context is the main obstacle for self-configuring pairing in wireless sensor networks. For this reason, most pairing methodologies rely on the use of custom commissioning tools, which require operation by specially trained personnel, in order to provide the missing context. In addition, these methodologies are time-consuming and require their redeployment after any system upgrade. Other methodologies make use of time-based radio frequency (RF) synchronization, which makes the network implementation too expensive, unusable for relatively large-scale deployment (e.g., networks deployed over a large infrastructure), and complicates efforts in realizing automated pairing.
Of all the components that define the context of a wireless sensor application, it is believed that location awareness is the most challenging and is the most important to obtain at the time of performing intelligent pairing.
It is known to employ secondary channels for context sensing in environments where global positioning system (GPS) signals are difficult to obtain (e.g., in indoor environments). One tracking system is based on both ultrasonic and radio signals, aimed at a location granularity of 15 cm. Every 200 ms, a controller sends out a radio message to all transceivers on mobile devices, indicating which transceiver should ping in the next time slot. Meanwhile, the controller also sends a reset message to the receivers' network, requesting them to be ready to pick up the incoming ultrasonic signal. Both location and orientation can be calculated. Unfortunately, this method is based on trilateration, which is an alternative to triangulation that relies upon distance measurements instead of angle measurements, and which requires an existing network infrastructure whose spatial coordinates are known. See Ward, A. et al., “A New Location Technique for the Active Office,” IEEE Personal Communications, vol. 4. no. 5, 11 pp. (October 1997); Harter, A. et al., “The Anatomy of a Context-Aware Application,” Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. 59-68 (August 1999).
Another location-support system also takes advantage of both ultrasonic and radio signals. Rather than the system tracking the user's location, however, each portable device determines its own location. The mobile device listens to two signals (one RF and one ultrasonic) that simultaneously originate from previously installed transmitters at known points by measuring the distance to a base station calculated by the time interval between the arrivals of two signals. The resulting location accuracy is a few centimeters of accuracy and angles to within 3-5 degrees of the true value. This approach, like the formerly described system, above, requires previous knowledge about the transmitter's spatial coordinates and a good scheduling for the transmitter's “beacons” (RF and ultrasound signals), in order to avoid collisions. See Priyantha, N. B. et al., “The Cricket Location-Support System,” Proceedings of the Sixth Annual ACM International Conference on Mobile Computing and Networking, 12 pp. (August 2000).
It is known to combine received signal strength indicator (RSSI) information and acoustic time of flight, in order to estimate distance using simultaneous beaconing. Inexpensive receivers are employed for detecting the beacons and, hence, systematic and calibration errors play a bigger role in affecting the accuracy of the system, which is minimized by a “mean calibration” technique. See Whitehouse, K. et al., “Calibration as Parameter Estimation in Sensor Networks,” Proceedings of the First ACM International Workshop on Sensor Networks and Applications, pp. 59-67 (September 2002).
It is known to employ RF and ultrasound in which a personal digital assistant (PDA) with two ultrasound receivers is able to detect a resource (like a printer) in a particular room. The resource emits a synchronized RF and ultrasound beacon upon request from the user via the PDA, and employs trilateration, such that the user is able to determine the location of the resource. It is assumed that the user knows (or has access to) the ID of the resource and manually activates the pairing procedure, which makes it inapplicable for wireless sensor networks. See Kindberg, T. et al., “Validating and Securing Spontaneous Associations between Wireless Devices,” Hewlett-Packard Company, pp. i, 1-6 (Sep. 12, 2002).
Another system employs only the RF signal strength of IEEE 802.11 transceivers as an indicator of the distance between a transmitter and a receiver without setting up an additional location tracking system. At the initial (off-line) phase, the system builds an RF signal strength database for a set of fixed receivers associated to known transmitter positions. During the normal operation, the RF signal strength of a transmitter is measured by the set of fixed receivers and is sent to a central computer, which examines the signal strength database and calculates the best fit for the current transmitter position based on a predefined RF propagation model. See Bahl, P. et al., “RADAR: An In-Building RF-based User Location and Tracking System,” Proc. IEEE INFOCOM, 10 pp. (March 2000).
There is room for improvement in systems for pairing wireless nodes and in methods for pairing wireless nodes of a communication network.